1. Field
Apparatuses and methods consistent with the exemplary embodiments relate to the field of multimedia communications, and more particularly, to the field of multimedia content retrieval using human hummed queries as an input.
2. Description of the Related Art
Related art methods use text as an input query, with additional parameters (e.g., genre, artist, and/or film) as classification types, in order to obtain a desirable list of multimedia content. Using such a query model can be a difficult proposition, as relevant input criteria may be unknown and may be inexact. The result may be a list which includes multiple copies, or may not include the user's desired list. With a growing collection of songs in the database, the problem becomes compounded.
The text-based query methods are limiting, as the content retrieval of songs are subjected to a text value identification of each record for retrieval of an appropriate match. This inefficiency is addressed by the use of Query by Humming (QBH) systems. A QBH system performs a content-based retrieval by which the input content is searched in a database of songs and the matching content is found. The input could be in the form of a hummed melody of a desired song. By using the hummed melody, the song from the database that has a matching content is retrieved. The QBH system is a fast and effective method that retrieves the song match by only using the melody.
Recent works relating to QBH have focused on retrieving this list based on melody representations, similarity scores and pitch contours. Recently developed QBH models require a database which contains a manual hum or tag referring to the original music files. The QBH models for hand-held devices require a database which contains a manual hum or tag referring to the original music files that are stored in a server. The user query is then sent to the database in order to match the corresponding song. Accordingly, there is no specific matching solution available to port the same model into hand-held devices which yields an optimized feature extraction and pattern matching process. In addition, when finding a match, the search could result in the same similarity for common searched files.